{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![HELP-Logo](https://avatars1.githubusercontent.com/u/7880370?s=200&v=4)\n",
    "\n",
    "\n",
    "# DMU 24 - XMM-LSS: Photo-z Selection Function\n",
    "\n",
    "The goal is to create a selection function for the photometric redshifts that varies spatially across the field. We will use the depth maps for the optical masterlist to find regions of the field that have similar photometric coverage and then calculate the fraction of sources meeting a given photo-z selection within those pixels.\n",
    "\n",
    "1. For optical depth maps: do clustering analysis to find HEALpix with similar photometric properties.\n",
    "2. Calculate selection function within those groups of similar regions as a function of magnitude in a given band.\n",
    "3. Paramatrise the selection function in such a way that it can be easily applied for a given sample of sources or region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This notebook was executed on: \n",
      "2018-06-28 16:18:08.640431\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "print(\"This notebook was executed on: \\n{}\".format(datetime.datetime.now()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(10, 6))\n",
    "\n",
    "import os\n",
    "import time\n",
    "\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column, Table, join\n",
    "import numpy as np\n",
    "import healpy as hp\n",
    "\n",
    "import warnings\n",
    "#We ignore warnings - this is a little dangerous but a huge number of warnings are generated by empty cells later\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from astropy.io.votable import parse_single_table\n",
    "from astropy.io import fits\n",
    "from astropy.stats import binom_conf_interval\n",
    "from astropy.utils.console import ProgressBar\n",
    "from astropy.modeling.fitting import LevMarLSQFitter\n",
    "\n",
    "from sklearn.cluster import MiniBatchKMeans, MeanShift\n",
    "from collections import Counter\n",
    "\n",
    "from astropy.modeling import Fittable1DModel, Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coords_to_hpidx(ra, dec, order):\n",
    "    \"\"\"Convert coordinates to HEALPix indexes\n",
    "    Given to list of right ascension and declination, this function computes\n",
    "    the HEALPix index (in nested scheme) at each position, at the given order.\n",
    "    Parameters\n",
    "    ----------\n",
    "    ra: array or list of floats\n",
    "        The right ascensions of the sources.\n",
    "    dec: array or list of floats\n",
    "        The declinations of the sources.\n",
    "    order: int\n",
    "        HEALPix order.\n",
    "    Returns\n",
    "    -------\n",
    "    array of int\n",
    "        The HEALPix index at each position.\n",
    "    \"\"\"\n",
    "    ra, dec = np.array(ra), np.array(dec)\n",
    "\n",
    "    theta = 0.5 * np.pi - np.radians(dec)\n",
    "    phi = np.radians(ra)\n",
    "    healpix_idx = hp.ang2pix(2**order, theta, phi, nest=True)\n",
    "\n",
    "    return healpix_idx\n",
    "\n",
    "class GLF1D(Fittable1DModel):\n",
    "    \"\"\"\n",
    "    Generalised Logistic Function \n",
    "    \"\"\"\n",
    "    inputs = ('x',)\n",
    "    outputs = ('y',)\n",
    "\n",
    "    A = Parameter()\n",
    "    B = Parameter()\n",
    "    K = Parameter()\n",
    "    Q = Parameter()\n",
    "    nu = Parameter()\n",
    "    M = Parameter()\n",
    "    \n",
    "    @staticmethod\n",
    "    def evaluate(x, A, B, K, Q, nu, M):\n",
    "        top = K - A\n",
    "        bottom = (1 + Q*np.exp(-B*(x-M)))**(1/nu)\n",
    "        return A + (top/bottom)\n",
    "\n",
    "    @staticmethod\n",
    "    def fit_deriv(x, A, B, K, Q, nu, M):\n",
    "        d_A = 1 - (1 + (Q*np.exp(-B*(x-M)))**(-1/nu))\n",
    "        \n",
    "        d_B = ((K - A) * (x-M) * (Q*np.exp(-B*(x-M)))) / (nu * ((1 + Q*np.exp(-B*(x-M)))**((1/nu) + 1)))\n",
    "\n",
    "        d_K = 1 + (Q*np.exp(-B*(x-M)))**(-1/nu)\n",
    "        \n",
    "        d_Q = -((K - A) * (Q*np.exp(-B*(x-M)))) / (nu * ((1 + Q*np.exp(-B*(x-M)))**((1/nu) + 1)))\n",
    "        \n",
    "        d_nu = ((K-A) * np.log(1 + (Q*np.exp(-B*(x-M))))) / ((nu**2) * ((1 + Q*np.exp(-B*(x-M)))**((1/nu))))\n",
    "        \n",
    "        d_M = -((K - A) * (Q*B*np.exp(-B*(x-M)))) / (nu * ((1 + Q*np.exp(-B*(x-M)))**((1/nu) + 1)))\n",
    "\n",
    "        return [d_A, d_B, d_K, d_Q, d_nu, d_M]\n",
    "    \n",
    "class InverseGLF1D(Fittable1DModel):\n",
    "    \"\"\"\n",
    "    Generalised Logistic Function \n",
    "    \"\"\"\n",
    "    inputs = ('x',)\n",
    "    outputs = ('y',)\n",
    "\n",
    "    A = Parameter()\n",
    "    B = Parameter()\n",
    "    K = Parameter()\n",
    "    Q = Parameter()\n",
    "    nu = Parameter()\n",
    "    M = Parameter()\n",
    "    \n",
    "    @staticmethod\n",
    "    def evaluate(x, A, B, K, Q, nu, M):\n",
    "        return M - (1/B)*(np.log((((K - A)/(x -A))**nu - 1)/Q))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0 - Set relevant initial parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "FIELD = 'XMM-LSS'\n",
    "ORDER = 10\n",
    "\n",
    "OUT_DIR = 'data'\n",
    "SUFFIX = 'depths_20180504_photoz_20180518'\n",
    "\n",
    "DEPTH_MAP = '../../dmu1/dmu1_ml_XMM-LSS/data/depths_xmm-lss_20180504.fits'\n",
    "MASTERLIST = '../../dmu1/dmu1_ml_XMM-LSS/data/master_catalogue_xmm-lss_20180221.fits'\n",
    "PHOTOZS = 'data/master_catalogue_xmm-lss_20180221_photoz_20180518_r_optimised.fits'\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I - Find clustering of healpix in the depth maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "depth_map = Table.read(DEPTH_MAP)\n",
    "\n",
    "# Get Healpix IDs\n",
    "hp_idx = depth_map['hp_idx_O_{0}'.format(ORDER)]\n",
    "\n",
    "# Calculate RA, Dec of depth map Healpix pixels for later plotting etc.\n",
    "dm_hp_ra, dm_hp_dec = hp.pix2ang(2**ORDER, hp_idx, nest=True, lonlat=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The depth map provides two measures of depth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_values = Table(depth_map.columns[2::2]) # Mean 1-sigma error within a cell\n",
    "p90_values = Table(depth_map.columns[3::2]) # 90th percentile of observed fluxes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the photo-z selection functions we will make use of the mean 1-sigma error as this can be used to accurately predict the completeness as a function of magnitude.\n",
    "\n",
    "We convert the mean 1-sigma uncertainty to a 3-sigma magnitude upper limit and convert to a useable array.\n",
    "When a given flux has no measurement in a healpix (and *ferr_mean* is therefore a *NaN*) we set the depth to some semi-arbitrary bright limit separate from the observed depths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dm_clustering = 23.9 - 2.5*np.log10(3*mean_values.to_pandas().as_matrix())\n",
    "dm_clustering[np.isnan(dm_clustering)] = 14\n",
    "dm_clustering[np.isinf(dm_clustering)] = 14\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To encourage the clustering to group nearby Healpix together, we also add the RA and Dec of the healpix to the inpux dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dm_clustering = np.hstack([dm_clustering, np.array(dm_hp_ra.data, ndmin=2).T, np.array(dm_hp_dec.data, ndmin=2).T])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we find clusters within the depth maps using a simple k-means clustering. For the number of clusters we assume an initial guess on the order of the number of different input magnitdues (/depths) in the dataset. This produces good initial results but may need further tuning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "NCLUSTERS = dm_clustering.shape[1]*2\n",
    "km = MiniBatchKMeans(n_clusters=NCLUSTERS)\n",
    "\n",
    "km.fit(dm_clustering)\n",
    "\n",
    "counts = Counter(km.labels_) # Quickly calculate sizes of the clusters for reference\n",
    "\n",
    "clusters = dict(zip(hp_idx.data, km.labels_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we illustrate the different clusters with each colour corresponding to a different group of similar sources (although the colours may not be unique to a single cluster of sources). You can see structures and patterns within the field, e.g. the outline of the HSC coverage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc392f0d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig, Ax = plt.subplots(1,1,figsize=(8,8))\n",
    "Ax.scatter(dm_hp_ra, dm_hp_dec, c=km.labels_, cmap=plt.cm.tab20, s=6)\n",
    "\n",
    "Ax.set_xlabel('Right Ascension [deg]')\n",
    "Ax.set_ylabel('Declination [deg]')\n",
    "Ax.set_title('{0}'.format(FIELD))\n",
    "Fig.savefig('plots/dmu24_{0}_sf_hpclusters_illustration.png'.format(FIELD), \n",
    "            format='png', bbox_inches='tight', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II - Map photo-z and masterlist objects to their corresponding depth cluster\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now load the photometric redshift catalog and keep only the key columns for this selection function.\n",
    "Note: if using a different photo-$z$ measure than the HELP standard `z1_median`, the relevant columns should be retained instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "photoz_catalogue = Table.read(PHOTOZS)\n",
    "\n",
    "photoz_catalogue.keep_columns(['help_id', 'RA', 'DEC', 'id', 'z1_median', 'z1_min', 'z1_max', 'z1_area'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we load the relevant sections of the masterlist catalog (including the magnitude columns) and map the Healpix values to their corresponding cluster. For each of the masterlist/photo-$z$ sources and their corresponding healpix we find the respective cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "masterlist_hdu = fits.open(MASTERLIST, memmap=True)\n",
    "masterlist = masterlist_hdu[1]\n",
    "\n",
    "masterlist_catalogue = Table()\n",
    "masterlist_catalogue['help_id'] = masterlist.data['help_id']\n",
    "masterlist_catalogue['RA'] = masterlist.data['ra']\n",
    "masterlist_catalogue['DEC'] = masterlist.data['dec']\n",
    "\n",
    "for column in masterlist.columns.names:\n",
    "    if (column.startswith('m_') or column.startswith('merr_')):\n",
    "        masterlist_catalogue[column] = masterlist.data[column]\n",
    "\n",
    "masterlist_hpx = coords_to_hpidx(masterlist_catalogue['RA'], masterlist_catalogue['DEC'], ORDER)\n",
    "\n",
    "keys = np.array([*clusters])\n",
    "incl = np.array([hpx in clusters.keys() for hpx in masterlist_hpx])\n",
    "if incl.sum() != len(incl):\n",
    "    notincl = np.where(np.invert(incl))[0]\n",
    "    for i in notincl:\n",
    "        masterlist_hpx[i] = keys[np.argmin(np.abs(masterlist_hpx[i] -keys))]\n",
    "    \n",
    "masterlist_catalogue[\"hp_idx_O_{:d}\".format(ORDER)] = masterlist_hpx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "masterlist_cl_no = np.array([clusters[hpx] for hpx in masterlist_hpx])\n",
    "masterlist_catalogue['hp_depth_cluster'] = masterlist_cl_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = join(masterlist_catalogue, photoz_catalogue, join_type='left', keys=['help_id', 'RA', 'DEC'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constructing the output selection function table:\n",
    "\n",
    "The photo-$z$ selection function will be saved in a table that mirrors the format of the input optical depth maps, with matching length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pz_depth_map = Table()\n",
    "pz_depth_map.add_column(depth_map['hp_idx_O_13'])\n",
    "pz_depth_map.add_column(depth_map['hp_idx_O_10'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III - Creating the binary photo-z selection function\n",
    "\n",
    "With the sources now easily grouped into regions of similar photometric properties, we can calculate the photo-$z$ selection function within each cluster of pixels. To begin with we want to create the most basic set of photo-$z$ selection functions - a map of the fraction of sources in the masterlist in a given region that have a photo-$z$ estimate. We will then create more informative selection function maps that make use of the added information from clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "194"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NCLUSTERS # Fixed during the clustering stage above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 In cluster: 17874  With photo-z: 13118     Fraction: 0.734 \n",
      "1 In cluster: 45101  With photo-z: 30188     Fraction: 0.669 \n",
      "2 In cluster: 159517 With photo-z: 77694     Fraction: 0.487 \n",
      "3 In cluster: 44288  With photo-z: 36292     Fraction: 0.819 \n",
      "4 In cluster: 15712  With photo-z: 13293     Fraction: 0.846 \n",
      "5 In cluster: 8595   With photo-z: 6664      Fraction: 0.775 \n",
      "6 In cluster: 13614  With photo-z: 11015     Fraction: 0.809 \n",
      "7 In cluster: 2493   With photo-z: 1982      Fraction: 0.795 \n",
      "8 In cluster: 103077 With photo-z: 77685     Fraction: 0.754 \n",
      "9 In cluster: 88023  With photo-z: 61090     Fraction: 0.694 \n",
      "10 In cluster: 72413  With photo-z: 58218     Fraction: 0.804 \n",
      "11 In cluster: 75367  With photo-z: 56652     Fraction: 0.752 \n",
      "12 In cluster: 11397  With photo-z: 8036      Fraction: 0.705 \n",
      "13 In cluster: 9288   With photo-z: 8093      Fraction: 0.871 \n",
      "14 In cluster: 24889  With photo-z: 20609     Fraction: 0.828 \n",
      "15 In cluster: 61957  With photo-z: 50376     Fraction: 0.813 \n",
      "16 In cluster: 62749  With photo-z: 44152     Fraction: 0.704 \n",
      "17 In cluster: 48661  With photo-z: 36883     Fraction: 0.758 \n",
      "18 In cluster: 41222  With photo-z: 33455     Fraction: 0.812 \n",
      "19 In cluster: 34958  With photo-z: 25230     Fraction: 0.722 \n",
      "20 In cluster: 35592  With photo-z: 30526     Fraction: 0.858 \n",
      "21 In cluster: 4664   With photo-z: 3370      Fraction: 0.723 \n",
      "22 In cluster: 15600  With photo-z: 12896     Fraction: 0.827 \n",
      "23 In cluster: 77550  With photo-z: 48102     Fraction: 0.620 \n",
      "24 In cluster: 76360  With photo-z: 54365     Fraction: 0.712 \n",
      "25 In cluster: 9027   With photo-z: 6754      Fraction: 0.748 \n",
      "26 In cluster: 26365  With photo-z: 12235     Fraction: 0.464 \n",
      "27 In cluster: 2008   With photo-z: 1707      Fraction: 0.850 \n",
      "28 In cluster: 203252 With photo-z: 132030    Fraction: 0.650 \n",
      "29 In cluster: 47912  With photo-z: 36853     Fraction: 0.769 \n",
      "30 In cluster: 48437  With photo-z: 39728     Fraction: 0.820 \n",
      "31 In cluster: 151085 With photo-z: 111915    Fraction: 0.741 \n",
      "32 In cluster: 164564 With photo-z: 78681     Fraction: 0.478 \n",
      "33 In cluster: 10147  With photo-z: 860       Fraction: 0.085 \n",
      "34 In cluster: 20822  With photo-z: 16240     Fraction: 0.780 \n",
      "35 In cluster: 19499  With photo-z: 14529     Fraction: 0.745 \n",
      "36 In cluster: 14698  With photo-z: 9230      Fraction: 0.628 \n",
      "37 In cluster: 2825   With photo-z: 1705      Fraction: 0.604 \n",
      "38 In cluster: 5938   With photo-z: 4374      Fraction: 0.737 \n",
      "39 In cluster: 20956  With photo-z: 16223     Fraction: 0.774 \n",
      "40 In cluster: 32837  With photo-z: 22160     Fraction: 0.675 \n",
      "41 In cluster: 10273  With photo-z: 7774      Fraction: 0.757 \n",
      "42 In cluster: 120528 With photo-z: 59851     Fraction: 0.497 \n",
      "43 In cluster: 127302 With photo-z: 91411     Fraction: 0.718 \n",
      "44 In cluster: 9656   With photo-z: 5307      Fraction: 0.550 \n",
      "45 In cluster: 27834  With photo-z: 22962     Fraction: 0.825 \n",
      "46 In cluster: 16113  With photo-z: 13692     Fraction: 0.850 \n",
      "47 In cluster: 27693  With photo-z: 20538     Fraction: 0.742 \n",
      "48 In cluster: 7951   With photo-z: 6705      Fraction: 0.843 \n",
      "49 In cluster: 16510  With photo-z: 13030     Fraction: 0.789 \n",
      "50 In cluster: 20807  With photo-z: 14450     Fraction: 0.694 \n",
      "51 In cluster: 16894  With photo-z: 12445     Fraction: 0.737 \n",
      "52 In cluster: 8874   With photo-z: 5547      Fraction: 0.625 \n",
      "53 In cluster: 29485  With photo-z: 22638     Fraction: 0.768 \n",
      "54 In cluster: 13606  With photo-z: 9002      Fraction: 0.662 \n",
      "55 In cluster: 54799  With photo-z: 38510     Fraction: 0.703 \n",
      "56 In cluster: 211080 With photo-z: 154946    Fraction: 0.734 \n",
      "57 In cluster: 16484  With photo-z: 12700     Fraction: 0.770 \n",
      "58 In cluster: 61475  With photo-z: 44099     Fraction: 0.717 \n",
      "59 In cluster: 31275  With photo-z: 22536     Fraction: 0.721 \n",
      "60 In cluster: 86410  With photo-z: 68028     Fraction: 0.787 \n",
      "61 In cluster: 20375  With photo-z: 14129     Fraction: 0.693 \n",
      "62 In cluster: 3005   With photo-z: 900       Fraction: 0.300 \n",
      "63 In cluster: 24751  With photo-z: 18464     Fraction: 0.746 \n",
      "64 In cluster: 2031   With photo-z: 1522      Fraction: 0.749 \n",
      "65 In cluster: 9020   With photo-z: 5584      Fraction: 0.619 \n",
      "66 In cluster: 3807   With photo-z: 2775      Fraction: 0.729 \n",
      "67 In cluster: 5047   With photo-z: 3894      Fraction: 0.772 \n",
      "68 In cluster: 11097  With photo-z: 5775      Fraction: 0.520 \n",
      "69 In cluster: 24537  With photo-z: 21131     Fraction: 0.861 \n",
      "70 In cluster: 11176  With photo-z: 9736      Fraction: 0.871 \n",
      "71 In cluster: 24552  With photo-z: 19984     Fraction: 0.814 \n",
      "72 In cluster: 52732  With photo-z: 39470     Fraction: 0.749 \n",
      "73 In cluster: 37245  With photo-z: 19946     Fraction: 0.536 \n",
      "74 In cluster: 38593  With photo-z: 33056     Fraction: 0.857 \n",
      "75 In cluster: 7552   With photo-z: 6331      Fraction: 0.838 \n",
      "76 In cluster: 24113  With photo-z: 17738     Fraction: 0.736 \n",
      "77 In cluster: 13965  With photo-z: 11290     Fraction: 0.808 \n",
      "78 In cluster: 98908  With photo-z: 73055     Fraction: 0.739 \n",
      "79 In cluster: 10743  With photo-z: 8687      Fraction: 0.809 \n",
      "80 In cluster: 33110  With photo-z: 27031     Fraction: 0.816 \n",
      "81 In cluster: 61675  With photo-z: 44666     Fraction: 0.724 \n",
      "82 In cluster: 95577  With photo-z: 73688     Fraction: 0.771 \n",
      "83 In cluster: 18127  With photo-z: 13638     Fraction: 0.752 \n",
      "84 In cluster: 64642  With photo-z: 46044     Fraction: 0.712 \n",
      "85 In cluster: 30062  With photo-z: 23308     Fraction: 0.775 \n",
      "86 In cluster: 8345   With photo-z: 6707      Fraction: 0.804 \n",
      "87 In cluster: 13002  With photo-z: 7943      Fraction: 0.611 \n",
      "88 In cluster: 7994   With photo-z: 5637      Fraction: 0.705 \n",
      "89 In cluster: 6929   With photo-z: 5306      Fraction: 0.766 \n",
      "90 In cluster: 13227  With photo-z: 8878      Fraction: 0.671 \n",
      "91 In cluster: 23247  With photo-z: 19050     Fraction: 0.819 \n",
      "92 In cluster: 11255  With photo-z: 7186      Fraction: 0.638 \n",
      "93 In cluster: 102155 With photo-z: 88174     Fraction: 0.863 \n",
      "94 In cluster: 8636   With photo-z: 7674      Fraction: 0.889 \n",
      "95 In cluster: 14890  With photo-z: 2482      Fraction: 0.167 \n",
      "96 In cluster: 30489  With photo-z: 19430     Fraction: 0.637 \n",
      "97 In cluster: 83420  With photo-z: 58924     Fraction: 0.706 \n",
      "98 In cluster: 5315   With photo-z: 4247      Fraction: 0.799 \n",
      "99 In cluster: 159479 With photo-z: 73375     Fraction: 0.460 \n",
      "100 In cluster: 15540  With photo-z: 9062      Fraction: 0.583 \n",
      "101 In cluster: 50765  With photo-z: 36558     Fraction: 0.720 \n",
      "102 In cluster: 66852  With photo-z: 27228     Fraction: 0.407 \n",
      "103 In cluster: 8690   With photo-z: 6416      Fraction: 0.738 \n",
      "104 In cluster: 75570  With photo-z: 57189     Fraction: 0.757 \n",
      "105 In cluster: 16053  With photo-z: 13462     Fraction: 0.839 \n",
      "106 In cluster: 19359  With photo-z: 14433     Fraction: 0.746 \n",
      "107 In cluster: 35448  With photo-z: 29975     Fraction: 0.846 \n",
      "108 In cluster: 34874  With photo-z: 21624     Fraction: 0.620 \n",
      "109 In cluster: 4887   With photo-z: 3383      Fraction: 0.692 \n",
      "110 In cluster: 55343  With photo-z: 42165     Fraction: 0.762 \n",
      "111 In cluster: 56407  With photo-z: 41105     Fraction: 0.729 \n",
      "112 In cluster: 4595   With photo-z: 3402      Fraction: 0.740 \n",
      "113 In cluster: 39375  With photo-z: 26961     Fraction: 0.685 \n",
      "114 In cluster: 36018  With photo-z: 28299     Fraction: 0.786 \n",
      "115 In cluster: 92388  With photo-z: 64103     Fraction: 0.694 \n",
      "116 In cluster: 10692  With photo-z: 9309      Fraction: 0.871 \n",
      "117 In cluster: 82117  With photo-z: 55289     Fraction: 0.673 \n",
      "118 In cluster: 38170  With photo-z: 32441     Fraction: 0.850 \n",
      "119 In cluster: 27713  With photo-z: 22440     Fraction: 0.810 \n",
      "120 In cluster: 12631  With photo-z: 8356      Fraction: 0.662 \n",
      "121 In cluster: 45034  With photo-z: 27692     Fraction: 0.615 \n",
      "122 In cluster: 4734   With photo-z: 3954      Fraction: 0.835 \n",
      "123 In cluster: 8472   With photo-z: 6983      Fraction: 0.824 \n",
      "124 In cluster: 28508  With photo-z: 19394     Fraction: 0.680 \n",
      "125 In cluster: 3292   With photo-z: 2543      Fraction: 0.772 \n",
      "126 In cluster: 6280   With photo-z: 4821      Fraction: 0.768 \n",
      "127 In cluster: 18399  With photo-z: 15944     Fraction: 0.867 \n",
      "128 In cluster: 161242 With photo-z: 128495    Fraction: 0.797 \n",
      "129 In cluster: 13393  With photo-z: 9282      Fraction: 0.693 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130 In cluster: 819    With photo-z: 588       Fraction: 0.718 \n",
      "131 In cluster: 106425 With photo-z: 88304     Fraction: 0.830 \n",
      "132 In cluster: 11506  With photo-z: 9181      Fraction: 0.798 \n",
      "133 In cluster: 3409   With photo-z: 2650      Fraction: 0.777 \n",
      "134 In cluster: 6923   With photo-z: 5792      Fraction: 0.837 \n",
      "135 In cluster: 16571  With photo-z: 14087     Fraction: 0.850 \n",
      "136 In cluster: 29027  With photo-z: 21266     Fraction: 0.733 \n",
      "137 In cluster: 91826  With photo-z: 66192     Fraction: 0.721 \n",
      "138 In cluster: 12615  With photo-z: 8094      Fraction: 0.642 \n",
      "139 In cluster: 17451  With photo-z: 14085     Fraction: 0.807 \n",
      "140 In cluster: 107645 With photo-z: 89934     Fraction: 0.835 \n",
      "141 In cluster: 52631  With photo-z: 34457     Fraction: 0.655 \n",
      "142 In cluster: 9408   With photo-z: 7834      Fraction: 0.833 \n",
      "143 In cluster: 21753  With photo-z: 16318     Fraction: 0.750 \n",
      "144 In cluster: 38501  With photo-z: 18348     Fraction: 0.477 \n",
      "145 In cluster: 132254 With photo-z: 99138     Fraction: 0.750 \n",
      "146 In cluster: 35118  With photo-z: 24861     Fraction: 0.708 \n",
      "147 In cluster: 46189  With photo-z: 38308     Fraction: 0.829 \n",
      "148 In cluster: 614897 With photo-z: 319365    Fraction: 0.519 \n",
      "149 In cluster: 3858   With photo-z: 2531      Fraction: 0.656 \n",
      "150 In cluster: 389273 With photo-z: 300032    Fraction: 0.771 \n",
      "151 In cluster: 11256  With photo-z: 8372      Fraction: 0.744 \n",
      "152 In cluster: 12119  With photo-z: 8356      Fraction: 0.689 \n",
      "153 In cluster: 13346  With photo-z: 8785      Fraction: 0.658 \n",
      "154 In cluster: 6176   With photo-z: 4288      Fraction: 0.694 \n",
      "155 In cluster: 16515  With photo-z: 7083      Fraction: 0.429 \n",
      "156 In cluster: 25510  With photo-z: 19367     Fraction: 0.759 \n",
      "157 In cluster: 8906   With photo-z: 6120      Fraction: 0.687 \n",
      "158 In cluster: 30054  With photo-z: 22218     Fraction: 0.739 \n",
      "159 In cluster: 33866  With photo-z: 28714     Fraction: 0.848 \n",
      "160 In cluster: 13288  With photo-z: 9537      Fraction: 0.718 \n",
      "161 In cluster: 36744  With photo-z: 28189     Fraction: 0.767 \n",
      "162 In cluster: 24891  With photo-z: 22264     Fraction: 0.894 \n",
      "163 In cluster: 6243   With photo-z: 4637      Fraction: 0.743 \n",
      "164 In cluster: 151555 With photo-z: 112134    Fraction: 0.740 \n",
      "165 In cluster: 154960 With photo-z: 132499    Fraction: 0.855 \n",
      "166 In cluster: 7518   With photo-z: 5211      Fraction: 0.693 \n",
      "167 In cluster: 5037   With photo-z: 4091      Fraction: 0.812 \n",
      "168 In cluster: 71061  With photo-z: 54142     Fraction: 0.762 \n",
      "169 In cluster: 34051  With photo-z: 19206     Fraction: 0.564 \n",
      "170 In cluster: 12669  With photo-z: 8205      Fraction: 0.648 \n",
      "171 In cluster: 24161  With photo-z: 16521     Fraction: 0.684 \n",
      "172 In cluster: 160731 With photo-z: 125764    Fraction: 0.782 \n",
      "173 In cluster: 3498   With photo-z: 2590      Fraction: 0.740 \n",
      "174 In cluster: 14091  With photo-z: 10770     Fraction: 0.764 \n",
      "175 In cluster: 12335  With photo-z: 8572      Fraction: 0.695 \n",
      "176 In cluster: 4083   With photo-z: 3407      Fraction: 0.834 \n",
      "177 In cluster: 3893   With photo-z: 2362      Fraction: 0.607 \n",
      "178 In cluster: 11275  With photo-z: 8739      Fraction: 0.775 \n",
      "179 In cluster: 221465 With photo-z: 123091    Fraction: 0.556 \n",
      "180 In cluster: 17358  With photo-z: 12315     Fraction: 0.709 \n",
      "181 In cluster: 105814 With photo-z: 92966     Fraction: 0.879 \n",
      "182 In cluster: 128718 With photo-z: 107473    Fraction: 0.835 \n",
      "183 In cluster: 8184   With photo-z: 6596      Fraction: 0.806 \n",
      "184 In cluster: 10989  With photo-z: 8349      Fraction: 0.760 \n",
      "185 In cluster: 13704  With photo-z: 10483     Fraction: 0.765 \n",
      "186 In cluster: 24965  With photo-z: 19000     Fraction: 0.761 \n",
      "187 In cluster: 21385  With photo-z: 17098     Fraction: 0.800 \n",
      "188 In cluster: 45860  With photo-z: 20923     Fraction: 0.456 \n",
      "189 In cluster: 128197 With photo-z: 88516     Fraction: 0.690 \n",
      "190 In cluster: 22441  With photo-z: 14355     Fraction: 0.640 \n",
      "191 In cluster: 32727  With photo-z: 26732     Fraction: 0.817 \n",
      "192 In cluster: 111647 With photo-z: 86284     Fraction: 0.773 \n",
      "193 In cluster: 19318  With photo-z: 15796     Fraction: 0.818 \n"
     ]
    }
   ],
   "source": [
    "cluster_photoz_fraction = np.ones(NCLUSTERS)\n",
    "\n",
    "pz_frac_cat = np.zeros(len(merged))\n",
    "pz_frac_map = np.zeros(len(dm_hp_ra))\n",
    "\n",
    "for ic, cluster in enumerate(np.arange(NCLUSTERS)):\n",
    "    ml_sources = (merged['hp_depth_cluster'] == cluster)\n",
    "    has_photoz = (merged['z1_median'] > -90.)\n",
    "    \n",
    "    in_ml = np.float(ml_sources.sum())\n",
    "    withz = np.float((ml_sources*has_photoz).sum())\n",
    "    \n",
    "    if in_ml > 0:\n",
    "        frac = withz / in_ml \n",
    "    else:\n",
    "        frac = 0.\n",
    "    \n",
    "    cluster_photoz_fraction[ic] = frac\n",
    "    print(\"\"\"{0} In cluster: {1:<6.0f} With photo-z: {2:<6.0f}\\\n",
    "    Fraction: {3:<6.3f}\"\"\".format(cluster, in_ml, withz, frac))\n",
    "    \n",
    "    # Map fraction to catalog positions for reference\n",
    "    where_cat = (merged['hp_depth_cluster'] == cluster)\n",
    "    pz_frac_cat[where_cat] = frac\n",
    "    \n",
    "    # Map fraction back to depth map healpix \n",
    "    where_map = (km.labels_ == cluster)\n",
    "    pz_frac_map[where_map] = frac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The binary photo-$z$ selection function of the field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc392b61198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fig, Ax = plt.subplots(1,1,figsize=(9.5,8))\n",
    "Sc = Ax.scatter(dm_hp_ra, dm_hp_dec, c=pz_frac_map, cmap=plt.cm.viridis, s=6, vmin=0, vmax=1)\n",
    "\n",
    "Ax.set_xlabel('Right Ascension [deg]')\n",
    "Ax.set_ylabel('Declination [deg]')\n",
    "Ax.set_title('{0}'.format(FIELD))\n",
    "CB = Fig.colorbar(Sc)\n",
    "CB.set_label('Fraction with photo-z estimate')\n",
    "Fig.savefig('plots/dmu24_{0}_sf_binary_map.png'.format(FIELD), \n",
    "            format='png', bbox_inches='tight', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the binary photo-$z$ selection function to output catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pz_depth_map.add_column(Column(name='pz_fraction', data=pz_frac_map))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## IV - Magnitude dependent photo-z selection functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The binary selection function gives a broad illustration of where photo-$z$s are available in the field (given the availability of optical datasets etc.). However, the fraction of sources that have an estimate available will depend on the brightness of a given source in the bands used for photo-$z$s.\n",
    "Furthermore, the quality of those photo-$z$ is also highly dependent on the depth, wavelength coverage and sampling of the optical data in that region.\n",
    "\n",
    "To calculate the likelihood of a given source having a photo-$z$ that passes the defined quality selection or be able to select samples of homogeneous photo-$z$ quality, we therefore need to estimate the magnitude (and spatially) dependent selection function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the photo-$z$ quality criteria\n",
    "\n",
    "A key stage in the photo-$z$ estimation methodology is the explicit calibration of the redshift posteriors as a function of magnitude. The benefit of this approach is that by making a cut based on the width of redshift posterior, $P(z)$, we can select sources with a desired estimated redshift precision.\n",
    "Making this cut based on the full $P(z)$ is impractical. However the main photo-$z$ catalog contains information about the width of the primary and secondary peaks above the 80% highest probability density (HPD) credible interval, we can use this information to determine our redshift quality criteria."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse columns to select the available magnitudes within the masterlist:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95 magnitude columns present in the masterlist.\n"
     ]
    }
   ],
   "source": [
    "filters = [col for col in merged.colnames if col.startswith('m_')]\n",
    "print('{0} magnitude columns present in the masterlist.'.format(len(filters)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_photoz_error = (0.5*(merged['z1_max']- merged['z1_min'])) / (1 + merged['z1_median'])\n",
    "\n",
    "photoz_quality_cut = (scaled_photoz_error < 0.2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the magnitude dependent selection function in a given masterlist filter, for each of the Healpix clusters we do the following:\n",
    "1. Find the number of masterlist sources within that cluster that have a measurement in the corresponding filter. (If this is zero - see stage 3B)\n",
    "2. Calculate the fraction of sources at that magnitude that haThis relation typically follows a form of a sigmoid function the declines towards fainter magnitudes - however depending on the selection being applied it may not start at 1. Similarly, the rate of decline and the turnover point depends on the depth of the optical selection properties of that cluster.\n",
    "3. \n",
    "    1. Fit the magnitude dependence using the generalised logistic function (GLF, or Richards' function). Provided with conservative boundary conditions and plausible starting conditions based on easily estimated properties (i.e. the typical magnitude in the cluster and the maximum point), this function is able to describe well almost the full range of measured selection functions.\n",
    "    2. If no masterlist sources in the cluster have an observation in the filter - all parameters set to zero (with the GLF then returning zero for all magnitudes).\n",
    "4. Map the parameters estimated for a given healpix cluster back to the healpix belonging to that cluster."
   ]
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   ],
   "source": [
    "for photometry_band in filters:\n",
    "    print(photometry_band)\n",
    "    \n",
    "    pz_frac_cat = np.zeros(len(merged))\n",
    "    pz_M_map = np.zeros((len(dm_hp_ra),6))\n",
    "\n",
    "    if np.isnan(merged[photometry_band]).sum() < len(merged):\n",
    "        m001, m999 = np.nanpercentile(merged[photometry_band], [0.1, 99.9])\n",
    "\n",
    "\n",
    "        counts, binedges = np.histogram(merged[photometry_band], \n",
    "                                        range=(np.minimum(m001, 17.), np.minimum(m999, 29.)),\n",
    "                                       bins=10)\n",
    "\n",
    "        binmids = 0.5*(binedges[:-1] + binedges[1:])\n",
    "\n",
    "\n",
    "        with ProgressBar(NCLUSTERS, ipython_widget=True) as bar:\n",
    "            for ic, cluster in enumerate(np.arange(NCLUSTERS)[:]):\n",
    "                ml_sources = (merged['hp_depth_cluster'] == cluster)\n",
    "                has_photoz = (merged['z1_median'] > -90.) * photoz_quality_cut\n",
    "                has_mag = (merged[photometry_band] > -90.)\n",
    "\n",
    "                in_ml = np.float(ml_sources.sum())\n",
    "                withz = (has_photoz)\n",
    "\n",
    "                frac = []\n",
    "                frac_upper = []\n",
    "                frac_lower = []\n",
    "\n",
    "                iqr25_mag = (np.nanpercentile(merged[photometry_band][ml_sources*has_photoz], 25))\n",
    "\n",
    "                if (ml_sources*has_photoz*has_mag).sum() > 1:\n",
    "                    for i in np.arange(len(binedges[:-1])):\n",
    "                        mag_cut = np.logical_and(merged[photometry_band] >= binedges[i],\n",
    "                                                 merged[photometry_band] < binedges[i+1])\n",
    "\n",
    "                        if (ml_sources * mag_cut).sum() > 0:\n",
    "                            pass_cut = np.sum(ml_sources * withz * mag_cut)\n",
    "                            total_cut = np.sum(ml_sources * mag_cut)\n",
    "                            frac.append(np.float(pass_cut) / total_cut)\n",
    "\n",
    "                            lower, upper = binom_conf_interval(pass_cut, total_cut)\n",
    "                            frac_lower.append(lower)\n",
    "                            frac_upper.append(upper)\n",
    "\n",
    "                        else:\n",
    "                            frac.append(0.)\n",
    "                            frac_lower.append(0.)\n",
    "                            frac_upper.append(1.)\n",
    "\n",
    "                    frac = np.array(frac)\n",
    "                    frac_upper = np.array(frac_upper)\n",
    "                    frac_lower = np.array(frac_lower)\n",
    "\n",
    "\n",
    "                    model = GLF1D(A=np.median(frac[:5]), K=0., B=0.9, Q=1., nu=0.4, M=iqr25_mag, \n",
    "                                  bounds={'A': (0,1), 'K': (0,1), 'B': (0., 5.),\n",
    "                                          'M': (np.minimum(m001, 17.), np.minimum(m999, 29.)),\n",
    "                                          'Q': (0., 10.),\n",
    "                                          'nu': (0, None)})\n",
    "\n",
    "                    fit = LevMarLSQFitter()\n",
    "                    m = fit(model, x=binmids, y=frac, maxiter=1000,\n",
    "                            weights=1/(0.5*((frac_upper-frac) + (frac-frac_lower))), \n",
    "                            estimate_jacobian=False)\n",
    "                    parameters = np.copy(m.parameters)\n",
    "\n",
    "                else:\n",
    "                    frac = np.zeros(len(binmids))\n",
    "                    frac_upper = np.zeros(len(binmids))\n",
    "                    frac_lower = np.zeros(len(binmids))\n",
    "\n",
    "                    parameters = np.zeros(6)\n",
    "\n",
    "                # Map parameters to cluster\n",
    "\n",
    "                # Map parameters back to depth map healpix \n",
    "                where_map = (km.labels_ == cluster)\n",
    "                pz_M_map[where_map] = parameters\n",
    "\n",
    "                bar.update()\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        pz_M_map = [np.zeros(6)] * len(dm_hp_ra)\n",
    "        \n",
    "    c = Column(data=pz_M_map, name='pz_glf_{0}'.format(photometry_band), shape=(1,6))\n",
    "\n",
    "    try:\n",
    "        pz_depth_map.add_column(c)\n",
    "    except:\n",
    "        pz_depth_map.replace_column('pz_glf_{0}'.format(photometry_band), c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The selection function catalog consists of a set of parameters for the generalised logistic function (GLF, or Richards' function) that can be used to calculate the fraction of masterlist sources that have a photo-$z$ estimate satisfying the quality cut as a function of a given magnitude. e.g.\n",
    "\n",
    "$S = \\rm{GLF}(M_{f}, \\textbf{P}_{\\rm{Healpix}})$,\n",
    "\n",
    "where $S$ is the success fraction for a given magnitude $M_{f}$ in a given filter, $f$, and $\\textbf{P}_{\\rm{Healpix}}$ corresponds to the set of 6 parameters fit for that healpix.\n",
    "\n",
    "\n",
    "In practical terms, using the GLF function defined in this notebook this would be `S = GLF1D(*P)(M)`. Similarly, to estimate the magnitude corresponding to a desired photo-$z$ completeness one can use the same parameters and the corresponding inverse function: `M = InverseGLF1D(*P)(S)`.\n",
    "\n",
    "### Save the photo-$z$ selection function catalog:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pz_depth_map.write('{0}/photo-z_selection_{1}_{2}.fits'.format(OUT_DIR, FIELD, SUFFIX).lower(), format='fits', overwrite=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ![HELP LOGO](https://avatars1.githubusercontent.com/u/7880370?s=75&v=4)\n",
    " \n",
    "**Author**: [Kenneth Duncan](http://dunkenj.github.io)\n",
    "\n",
    "The Herschel Extragalactic Legacy Project, ([HELP](http://herschel.sussex.ac.uk/)), is a [European Commission Research Executive Agency](https://ec.europa.eu/info/departments/research-executive-agency_en)\n",
    "funded project under the SP1-Cooperation, Collaborative project, Small or medium-scale focused research project, FP7-SPACE-2013-1 scheme, Grant Agreement\n",
    "Number 607254.\n",
    "\n",
    "[Acknowledgements](http://herschel.sussex.ac.uk/acknowledgements)\n",
    "\n"
   ]
  }
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